Situating the eco-social economy: conservation initiatives and environmental organizations as catalysts for social and economic development
Bibliographic record
Abstract
The social economy is a third sector of the economy, besides the public and private sectors, that provides critical social and economic services to society. Though there is broad recognition that both society and economy are dependent on functioning and healthy ecosystems, theories and definitions of the social economy rarely include reference to environmental and conservation-focused activities or outcomes. This paper empirically situates the concept of an eco-social economy within the context of a community conservation initiative. Through a case study of the Lutsel K'e Dene First Nation and the Thaidene Nene Protected Area in northern Canada, this paper demonstrates that: (i) for indigenous people, conservation is as much a social, economic, political, and cultural endeavour as it is about the protection of nature; (ii) outside environmental non-governmental organizations are also aligning their conservation mandates with the broader social, economic, and cultural goals of northern indigenous communities; and (iii) local social economy organizations are emerging to advocate for conservation as a means to achieve social and economic development ends. These examples compel us to envisage a social economy that incorporates environmental organizations and conservation initiatives and movements and that makes explicit a distinct eco-social economy. This theoretical concept has global applicability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".